Approximation Algorithms for Broadcasting in Flower Graphs

Over the last century, telecommunication networks have become the nervous system of our society. As data is generated and stored on varied nodes, effective communication is imperative to ensure efficient use of the network. Our ever-growing reliance on these increasingly large and complex networks make ineffective communication strategies evermore apparent. Broadcasting is a fundamental information-dissemination problem which models communication across a connected graph in the following manner: a single vertex, the originator, seeks to pass some message along to all other vertices in the graph. In general, research on broadcasting can be grouped in roughly two categories: Firstly, given some particular graph and some particular vertex chosen to be originator, what is a broadcast scheme that informs the entire graph in the minimum time possible? Secondly, given some number of nodes, how can we arrange them in a particular network topology such that we can achieve minimal broadcast time from any vertex? This thesis focuses on problems of the first category. Finding the minimum broadcast time of any vertex in an arbitrary graph is NP-Complete, but efficient algorithms have been found for particular graph families. In particular, polynomial time algorithms have been found for trees and some tree-like graphs: unicyclic graphs, tree of cycles. Such algorithms have also been found for some graphs with no intersecting cliques, such as fully connected trees and trees of cliques. Finally, graphs containing cycles with particular restrictions were also studied, and efficient algorithms for necklace graphs and k-restricted cactus graphs were also found. The question still stands however, of whether these restrictions may be too conservative, and that efficient algorithms exist on broader classes of graphs. In particular, significant research has been made towards finding an efficient broadcasting algorithm on cactus graphs, which has not been found so far. This thesis studies the broadcasting problem on Flower graphs, which capture the difficulty of cactus graphs in a simple graph family. Flower graphs, or k-cycle graphs, are graphs composed of k cycles all joined on a single central vertex v_c. The contributions of this thesis for broadcasting on flower graphs is two-fold: it first improves the approximation ratio for broadcasting on flower graphs. It then provides a heuristic which performs significantly better in practice than the current best heuristic. We also demonstrate that our heuristic finds the optimal broadcast time for particular subcases of flower graphs

Problems Related to Classical and Universal List Broadcasting

Broadcasting is a fundamental problem in the information dissemination area. In classical broadcasting, a message must be sent from one network member to all other members as rapidly as feasible. Although it has been demonstrated that this problem is NP-Hard for arbitrary graphs, it has several applications in various fields. As a result, the universal lists model, replicating real-world restrictions like the memory limits of nodes in large networks, is introduced as a branch of this problem in the literature. In the universal lists model, each node is equipped with a fixed list and has to follow the list regardless of the originator. In this study, we focus on both classical and universal lists broadcasting. Classical broadcasting is solvable for a few families of networks, such as trees, unicyclic graphs, tree of cycles, and tree of cliques. In this study, we begin by presenting an optimal algorithm that finds the broadcast time of any vertex in a Fully Connected Tree (FCT_n) in O(|V | log log n) time. An FCT_n is formed by attaching arbitrary trees to vertices of a complete graph of size n where |V| is the total number of vertices in the graph. Then, we replace the complete graph with a Hypercube H_k and propose a new heuristic for the Hypercube of Trees (HT_k). Not only does this heuristic have the same approximation ratio as the best-known algorithm, but our numerical results also show its superiority in most experiments. Our heuristic is able to outperform the current upper bound in up to 90% of the situations, resulting in an average speedup of 30%. Most importantly, our results illustrate that it can maintain its performance even if the network size grows, making the proposed heuristic practically useful. Afterward, we focus on broadcasting with universal lists, in which once a vertex is informed, it must follow its corresponding list, regardless of the originator and the neighbor from which it received the message. The problem of broadcasting with universal lists could be categorized into two sub-models: non-adaptive and adaptive. In the latter model, a sender will skip the vertices on its list from which it has received the message, while those vertices will not be skipped in the first model. In this study, we will present another sub-model called fully adaptive. Not only does this model benefit from a significantly better space complexity compared to the classical model, but, as will be proved, it is faster than the two other sub-models. Since the suggested model fits real-world network architectures, we will design optimal broadcast algorithms for well-known interconnection networks such as trees, grids, and cube-connected cycles. We also present an upper bound for tori under the same model. Then we focus on designing broadcast graphs (bg)’s under this model. A bg is a graph with minimum possible broadcast time from any originator. Additionally, a minimum broadcast graph (mbg) is a bg with the minimum possible number of edges. We propose mbg’s on n vertices for n ≤ 10 and sparse bg’s for 11 ≤ n ≤ 14 under the fully-adaptive model. Afterward, we introduce the first infinite families of bg’s under this model, and we prove that hypercubes are mbg under this model. Later, we establish the optimal broadcast time of k−ary trees and binomial trees under the nonadaptive model and provide an upper bound for complete bipartite graphs. We also improved a general upper bound for trees under the same model. We then suggest several general upper bounds for the universal lists by comparing them with the messy broadcasting model. Finally, we propose the first heuristic for this problem, namely HUB-GA: a Heuristic for Universal lists Broadcasting with Genetic Algorithm. We undertake various numerical experiments on frequently used interconnection networks in the literature, graphs with clique-like structures, and synthetic instances in order to cover many possibilities of industrial topologies. We also compare our results with state-of-the-art methods for classical broadcasting, which is proved to be the fastest model among all. Although the universal list model utilizes less memory than the classical model, our algorithm finds the same broadcast time as the classical model in diverse situations

Low Cost Quality of Service Multicast Routing in High Speed Networks

Many of the services envisaged for high speed networks, such as B-ISDN/ATM, will support real-time applications with large numbers of users. Examples of these types of application range from those used by closed groups, such as private video meetings or conferences, where all participants must be known to the sender, to applications used by open groups, such as video lectures, where partcipants need not be known by the sender. These types of application will require high volumes of network resources in addition to the real-time delay constraints on data delivery. For these reasons, several multicast routing heuristics have been proposed to support both interactive and distribution multimedia services, in high speed networks. The objective of such heuristics is to minimise the multicast tree cost while maintaining a real-time bound on delay. Previous evaluation work has compared the relative average performance of some of these heuristics and concludes that they are generally efficient, although some perform better for small multicast groups and others perform better for larger groups. Firstly, we present a detailed analysis and evaluation of some of these heuristics which illustrates that in some situations their average performance is reversed; a heuristic that in general produces efficient solutions for small multicasts may sometimes produce a more efficient solution for a particular large multicast, in a specific network. Also, in a limited number of cases using Dijkstra's algorithm produces the best result. We conclude that the efficiency of a heuristic solution depends on the topology of both the network and the multicast, and that it is difficult to predict. Because of this unpredictability we propose the integration of two heuristics with Dijkstra's shortest path tree algorithm to produce a hybrid that consistently generates efficient multicast solutions for all possible multicast groups in any network. These heuristics are based on Dijkstra's algorithm which maintains acceptable time complexity for the hybrid, and they rarely produce inefficient solutions for the same network/multicast. The resulting performance attained is generally good and in the rare worst cases is that of the shortest path tree. The performance of our hybrid is supported by our evaluation results. Secondly, we examine the stability of multicast trees where multicast group membership is dynamic. We conclude that, in general, the more efficient the solution of a heuristic is, the less stable the multicast tree will be as multicast group membership changes. For this reason, while the hybrid solution we propose might be suitable for use with closed user group multicasts, which are likely to be stable, we need a different approach for open user group multicasting, where group membership may be highly volatile. We propose an extension to an existing heuristic that ensures multicast tree stability where multicast group membership is dynamic. Although this extension decreases the efficiency of the heuristics solutions, its performance is significantly better than that of the worst case, a shortest path tree. Finally, we consider how we might apply the hybrid and the extended heuristic in current and future multicast routing protocols for the Internet and for ATM Networks.

Optimal transport on wireless networks

We present a study of the application of a variant of a recently introduced heuristic algorithm for the optimization of transport routes on complex networks to the problem of finding the optimal routes of communication between nodes on wireless networks. Our algorithm iteratively balances network traffic by minimizing the maximum node betweenness on the network. The variant we consider specifically accounts for the broadcast restrictions imposed by wireless communication by using a different betweenness measure. We compare the performance of our algorithm to two other known algorithms and find that our algorithm achieves the highest transport capacity both for minimum node degree geometric networks, which are directed geometric networks that model wireless communication networks, and for configuration model networks that are uncorrelated scale-free networks.Comment: 5 pages, 4 figure

Metaheuristic approaches for optimal broadcasting design in metropolitan MANETs

11th International Conference on Computer Aided Systems Theory. Las Palmas de Gran Canaria, Spain, February 12-16, 2007Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the duration of the operation itself. This research, which has been developed within the OPLINK project (http://oplink.lcc.uma.es), faces a wide study about this problem in metropolitan MANETs with up to seven different advanced multiobjective metaheuristics. They all compute Pareto fronts of solutions which empower a human designer with the ability of choosing the preferred configuration for the network. The quality of these fronts is evaluated by using the hypervolume metric. The obtained results show that the SPEA2 algorithm is the most accurate metaheuristic for solving the broadcasting problem.Publicad

Centralized and Cooperative Transmission of Secure Multiple Unicasts using Network Coding

We introduce a method for securely delivering a set of messages to a group of clients over a broadcast erasure channel where each client is interested in a distinct message. Each client is able to obtain its own message but not the others'. In the proposed method the messages are combined together using a special variant of random linear network coding. Each client is provided with a private set of decoding coefficients to decode its own message. Our method provides security for the transmission sessions against computational brute-force attacks and also weakly security in information theoretic sense. As the broadcast channel is assumed to be erroneous, the missing coded packets should be recovered in some way. We consider two different scenarios. In the first scenario the missing packets are retransmitted by the base station (centralized). In the second scenario the clients cooperate with each other by exchanging packets (decentralized). In both scenarios, network coding techniques are exploited to increase the total throughput. For the case of centralized retransmissions we provide an analytical approximation for the throughput performance of instantly decodable network coded (IDNC) retransmissions as well as numerical experiments. For the decentralized scenario, we propose a new IDNC based retransmission method where its performance is evaluated via simulations and analytical approximation. Application of this method is not limited to our special problem and can be generalized to a new class of problems introduced in this paper as the cooperative index coding problem